OpenRaiser/Intern-Atlas huggingface dataset:
The Atari Grand Challenge Dataset
Venue: arXiv cs.AI 2017
Year: 2017

Recent progress in Reinforcement Learning (RL), fueled by its combination, with Deep Learning has enabled impressive results in learning to interact with complex virtual environments, yet real-world applications of RL are still scarce. A key limitation is data efficiency, with current state-of-the-art approaches requiring millions of training samples. A promising way to tackle this problem is to augment RL with learning from human demonstrations. However, human demonstration data is not yet readily available. This hinders progress in this direction. The present work addresses this problem as follows. We (i) collect and describe a large dataset of human Atari 2600 replays -- the largest and most diverse such data set publicly released to date, (ii) illustrate an example use of this dataset by analyzing the relation between demonstration quality and imitation learning performance, and (iii) outline possible research directions that are opened up by our work.
Boundedness-Preserving Implicit Correction of Mesh-Induced Errors for VoF Based Heat and Mass Transfer
Venue: arXiv cs.CE 2017
Year: 2017

Spatial discretisation of geometrically complex computational domains often entails unstructured meshes of general topology for Computational Fluid Dynamics (CFD). Mesh skewness is then typically encountered causing severe deterioration of the formal order of accuracy of the discretisation, or boundedness of the solution, or both. Particularly methods inherently relying on the accurate and bounded transport of sharp fields suffer from all types of mesh-induced skewness errors, namely both non-orthogonality and non-conjunctionality errors. This work is devoted to a boundedness-preserving strategy to correct for skewness errors arising from discretisation of advection and diffusion terms within the context of interfacial heat and mass transfer based on the Volume-of-Fluid methodology. The implementation has been accomplished using a second-order finite volume method with support for unstructured meshes of general topology. We examine and advance suitable corrections for the finite volume discretisation of a consistent single-field model, where both accurate and bounded transport due to diffusion and advection is crucial. In order to ensure consistency of both the volume fraction and the species concentration transport, i.e. to avoid artificial heat or species transfer, corrections are studied for both cases. The cross interfacial jump and adjacent sharp gradients of species concentration render the correction for skewness-induced diffusion and advection errors additionally demanding and has not so far been addressed in the literature.
Learning When to Attend for Neural Machine Translation
Venue: arXiv cs.CL 2017
Year: 2017

In the past few years, attention mechanisms have become an indispensable component of end-to-end neural machine translation models. However, previous attention models always refer to some source words when predicting a target word, which contradicts with the fact that some target words have no corresponding source words. Motivated by this observation, we propose a novel attention model that has the capability of determining when a decoder should attend to source words and when it should not. Experimental results on NIST Chinese-English translation tasks show that the new model achieves an improvement of 0.8 BLEU score over a state-of-the-art baseline.
Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols
Venue: arXiv cs.LG 2017
Year: 2017

Learning to communicate through interaction, rather than relying on explicit supervision, is often considered a prerequisite for developing a general AI. We study a setting where two agents engage in playing a referential game and, from scratch, develop a communication protocol necessary to succeed in this game. Unlike previous work, we require that messages they exchange, both at train and test time, are in the form of a language (i.e. sequences of discrete symbols). We compare a reinforcement learning approach and one using a differentiable relaxation (straight-through Gumbel-softmax estimator) and observe that the latter is much faster to converge and it results in more effective protocols. Interestingly, we also observe that the protocol we induce by optimizing the communication success exhibits a degree of compositionality and variability (i.e. the same information can be phrased in different ways), both properties characteristic of natural languages. As the ultimate goal is to ensure that communication is accomplished in natural language, we also perform experiments where we inject prior information about natural language into our model and study properties of the resulting protocol.
A Learning Based Optimal Human Robot Collaboration with Linear Temporal Logic Constraints
Venue: arXiv cs.RO 2017
Year: 2017

This paper considers an optimal task allocation problem for human robot collaboration in human robot systems with persistent tasks. Such human robot systems consist of human operators and intelligent robots collaborating with each other to accomplish complex tasks that cannot be done by either part alone. The system objective is to maximize the probability of successfully executing persistent tasks that are formulated as linear temporal logic specifications and minimize the average cost between consecutive visits of a particular proposition. This paper proposes to model the human robot collaboration under a framework with the composition of multiple Markov Decision Process (MDP) with possibly unknown transition probabilities, which characterizes how human cognitive states, such as human trust and fatigue, stochastically change with the robot performance. Under the unknown MDP models, an algorithm is developed to learn the model and obtain an optimal task allocation policy that minimizes the expected average cost for each task cycle and maximizes the probability of satisfying linear temporal logic constraints. Moreover, this paper shows that the difference between the optimal policy based on the learned model and that based on the underlying ground truth model can be bounded by arbitrarily small constant and large confidence level with sufficient samples. The case study of an assembly process demonstrates the effectiveness and benefits of our proposed learning based human robot collaboration.
Aerial Grasping: Modeling and Control of a Flying Hand
Venue: arXiv cs.RO 2017
Year: 2017

In this paper, we present the design, simulation and experimental validation of a control architecture for a flying hand, i.e., a system made of an unmanned aerial vehicle, a robotic manipulator and a gripper, which is grasping an object fixed on a vertical wall. The goal of this work is to show that the overall control allows the flying hand to approach the wall, to dock on the object by means of the gripper, take the object and fly away. The control strategy has been implemented and validated in the simulated model and in experiments on the complete flying hand system.
Low-Rank Matrix Approximation in the Infinity Norm
Venue: arXiv cs.CC 2017
Year: 2017

The low-rank matrix approximation problem with respect to the entry-wise $\ell_{\infty}$-norm is the following: given a matrix $M$ and a factorization rank $r$, find a matrix $X$ whose rank is at most $r$ and that minimizes $\max_{i,j} |M_{ij} - X_{ij}|$. In this paper, we prove that the decision variant of this problem for $r=1$ is NP-complete using a reduction from the problem `not all equal 3SAT'. We also analyze several cases when the problem can be solved in polynomial time, and propose a simple practical heuristic algorithm which we apply on the problem of the recovery of a quantized low-rank matrix.
Megapixel Size Image Creation using Generative Adversarial Networks
Venue: arXiv cs.CV 2017
Year: 2017

Since its appearance, Generative Adversarial Networks (GANs) have received a lot of interest in the AI community. In image generation several projects showed how GANs are able to generate photorealistic images but the results so far did not look adequate for the quality standard of visual media production industry. We present an optimized image generation process based on a Deep Convolutional Generative Adversarial Networks (DCGANs), in order to create photorealistic high-resolution images (up to 1024x1024 pixels). Furthermore, the system was fed with a limited dataset of images, less than two thousand images. All these results give more clue about future exploitation of GANs in Computer Graphics and Visual Effects.
Green Virtualization for Multiple Collaborative Cellular Operators
Venue: arXiv cs.NI 2017
Year: 2017

This paper proposes and investigates a green virtualization framework for infrastructure sharing among multiple cellular operators whose networks are powered by a combination of conventional and renewable sources of energy. Under the proposed framework, the virtual network formed by unifying radio access infrastructures of all operators is optimized for minimum energy consumption by deactivating base stations (BSs) with low traffic loads. The users initially associated to those BSs are off-loaded to neighboring active ones. A fairness criterion for collaboration based on roaming prices is introduced to cover the additional energy costs incurred by host operators. The framework also ensures that any collaborating operator is not negatively affected by its participation in the proposed virtualization. A multi-objective linear programming problem is formulated to achieve energy and cost efficiency of the networks' operation by identifying the set of inter-operator roaming prices. For the case when collaboration among all operators is infeasible due to profitability, capacity, or power constraints, an iterative algorithm is proposed to determine the groups of operators that can viably collaborate. Results show significant energy savings using the proposed virtualization as compared to the standalone case. Moreover, collaborative operators exploiting locally generated renewable energy are rewarded more than traditional ones.
Optimal repair of Reed-Solomon codes: Achieving the cut-set bound
Venue: arXiv cs.IT 2017
Year: 2017

Coding for distributed storage gives rise to a new set of problems in coding theory related to the need of reducing inter-node communication in the system. A large number of recent papers addressed the problem of optimizing the total amount of information downloaded for repair of a single failed node (the repair bandwidth) by accessing information on $d$ {\em helper nodes}, where $k\le d\le n-1.$ By the so-called cut-set bound (Dimakis et al., 2010), the repair bandwidth of an $(n,k=n-r)$ MDS code using $d$ helper nodes is at least $dl/(d+1-k),$ where $l$ is the size of the node. Also, a number of known constructions of MDS array codes meet this bound with equality. In a related but separate line of work, Guruswami and Wootters (2016) studied repair of Reed-Solomon (RS) codes, showing that these codes can be repaired using a smaller bandwidth than under the trivial approach. At the same time, their work as well as follow-up papers stopped short of constructing RS codes (or any scalar MDS codes) that meet the cut-set bound with equality, which has been an open problem in coding theory. In this work we present a solution to this problem, constructing RS codes of length $n$ over the field $q^l, l=\exp((1+o(1))n\log n)$ that meet the cut-set bound. We also prove an almost matching lower bound on $l$, showing that the super-exponential scaling is both necessary and sufficient for achieving the cut-set bound using linear repair schemes. More precisely, we prove that for scalar MDS codes (including the RS codes) to meet this bound, the sub-packetization $l$ must satisfy $l \ge \exp((1+o(1)) k\log k).$
Diversified Top-k Partial MaxSAT Solving
Venue: arXiv cs.AI 2017
Year: 2017

We introduce a diversified top-k partial MaxSAT problem, a combination of partial MaxSAT problem and enumeration problem. Given a partial MaxSAT formula F and a positive integer k, the diversified top-k partial MaxSAT is to find k maximal solutions for F such that the k maximal solutions satisfy the maximum number of soft clauses of F. This problem can be widely used in many applications including community detection, sensor place, motif discovery, and combinatorial testing. We prove the problem is NP-hard and propose an approach for solving the problem. The concrete idea of the approach is to design an encoding EE which reduces diversified top-k partial MaxSAT problem into partial MaxSAT problem, and then solve the resulting problem with state-of-art solvers. In addition, we present an algorithm MEMKC exactly solving the diversified top-k partial MaxSAT. Through several experiments we show that our approach can be successfully applied to the interesting problem.
Faster Spatially Regularized Correlation Filters for Visual Tracking
Venue: arXiv cs.CV 2017
Year: 2017

Discriminatively learned correlation filters (DCF) have been widely used in online visual tracking filed due to its simplicity and efficiency. These methods utilize a periodic assumption of the training samples to construct a circulant data matrix, which implicitly increases the training samples and reduces both storage and computational complexity.The periodic assumption also introduces unwanted boundary effects. Recently, Spatially Regularized Correlation Filters (SRDCF) solved this issue by introducing penalization on correlation filter coefficients depending on their spatial location. However, SRDCF's efficiency dramatically decreased due to the breaking of circulant structure. We propose Faster Spatially Regularized Discriminative Correlation Filters (FSRDCF) for tracking. The FSRDCF is constructed from Ridge Regression, the circulant structure of training samples in the spatial domain is fully used, more importantly, we further exploit the circulant structure of regularization function in the Fourier domain, which allows our problem to be solved more directly and efficiently. Experiments are conducted on three benchmark datasets: OTB-2013, OTB-2015 and VOT2016. Our approach achieves equivalent performance to the baseline tracker SRDCF on all three datasets. On OTB-2013 and OTB-2015 datasets, our approach obtains a more than twice faster running speed and a more than third times shorter start-up time than the SRDCF. For state-of-the-art comparison, our approach demonstrates superior performance compared to other non-spatial-regularization trackers.
Coding Method for Parallel Iterative Linear Solver
Venue: arXiv cs.IT 2017
Year: 2017

Computationally intensive distributed and parallel computing is often bottlenecked by a small set of slow workers known as stragglers. In this paper, we utilize the emerging idea of "coded computation" to design a novel error-correcting-code inspired technique for solving linear inverse problems under specific iterative methods in a parallelized implementation affected by stragglers. Example applications include inverse problems in machine learning on graphs, such as personalized PageRank and sampling on graphs. We provably show that our coded-computation technique can reduce the mean-squared error under a computational deadline constraint. In fact, the ratio of mean-squared error of replication-based and coded techniques diverges to infinity as the deadline increases. Our experiments for personalized PageRank performed on real systems and real social networks show that this ratio can be as large as $10^4$. Further, unlike coded-computation techniques proposed thus far, our strategy combines outputs of all workers, including the stragglers, to produce more accurate estimates at the computational deadline. This also ensures that the accuracy degrades "gracefully" in the event that the number of stragglers is large.
Improved User Tracking in 5G Millimeter Wave Mobile Networks via Refinement Operations
Venue: arXiv cs.NI 2017
Year: 2017

The millimeter wave (mmWave) frequencies offer the availability of huge bandwidths to provide unprecedented data rates to next-generation cellular mobile terminals. However, directional mmWave links are highly susceptible to rapid channel variations and suffer from severe isotropic pathloss. To face these impairments, this paper addresses the issue of tracking the channel quality of a moving user, an essential procedure for rate prediction, efficient handover and periodic monitoring and adaptation of the user's transmission configuration. The performance of an innovative tracking scheme, in which periodic refinements of the optimal steering direction are alternated to sparser refresh events, are analyzed in terms of both achievable data rate and energy consumption, and compared to those of a state-of-the-art approach. We aim at understanding in which circumstances the proposed scheme is a valid option to provide a robust and efficient mobility management solution. We show that our procedure is particularly well suited to highly variant and unstable mmWave environments.
Order-Preserving Encryption Using Approximate Integer Common Divisors
Venue: arXiv cs.CR 2017
Year: 2017

We present a new, but simple, randomised order-preserving encryption (OPE) scheme based on the general approximate common divisor problem (GACDP). This appears to be the first OPE scheme to be based on a computational hardness primitive, rather than a security game. This scheme requires only $O(1)$ arithmetic operations for encryption and decryption. We show that the scheme has optimal information leakage under the assumption of uniformly distributed plaintexts, and we indicate that this property extends to some non-uniform distributions. We report on an extensive evaluation of our algorithms. The results clearly demonstrate highly favourable execution times in comparison with existing OPE schemes.
Bipedal locomotion using variable stiffness actuation
Venue: arXiv cs.RO 2017
Year: 2017

Robust and energy-efficient bipedal locomotion in robotics is still a challenging topic. In order to address issues in this field, we can take inspiration from nature, by studying human locomotion. The Spring-Loaded Inverted Pendulum (SLIP) model has shown to be a good model for this purpose. However, the human musculoskeletal system enables us to actively modulate leg stiffness, for example when walking in rough terrain with irregular and unexpected height variations of the walking surface. This ability of varying leg stiffness is not considered in conventional SLIP-based models, and therefore this paper explores the potential role of active leg stiffness variation in bipedal locomotion. It is shown that the conceptual SLIP model can be iteratively extended to more closely resemble a realistic (i.e., non-ideal) walker, and that feedback control strategies can be designed that reproduce the SLIP behavior in these extended models. We show that these extended models realize a cost of transport comparable to human walking, which indicates that active leg stiffness variation plays an important role in human locomotion that was previously not captured by the SLIP model. The results of this study show that active leg stiffness adaptation is a promising approach for realizing more energy-efficient and robust bipedal walking robots.
Grounding Symbols in Multi-Modal Instructions
Venue: arXiv cs.AI 2017
Year: 2017

As robots begin to cohabit with humans in semi-structured environments, the need arises to understand instructions involving rich variability---for instance, learning to ground symbols in the physical world. Realistically, this task must cope with small datasets consisting of a particular users' contextual assignment of meaning to terms. We present a method for processing a raw stream of cross-modal input---i.e., linguistic instructions, visual perception of a scene and a concurrent trace of 3D eye tracking fixations---to produce the segmentation of objects with a correspondent association to high-level concepts. To test our framework we present experiments in a table-top object manipulation scenario. Our results show our model learns the user's notion of colour and shape from a small number of physical demonstrations, generalising to identifying physical referents for novel combinations of the words.
A Concurrency-Agnostic Protocol for Multi-Paradigm Concurrent Debugging Tools
Venue: arXiv cs.PL 2017
Year: 2017

Today's complex software systems combine high-level concurrency models. Each model is used to solve a specific set of problems. Unfortunately, debuggers support only the low-level notions of threads and shared memory, forcing developers to reason about these notions instead of the high-level concurrency models they chose. This paper proposes a concurrency-agnostic debugger protocol that decouples the debugger from the concurrency models employed by the target application. As a result, the underlying language runtime can define custom breakpoints, stepping operations, and execution events for each concurrency model it supports, and a debugger can expose them without having to be specifically adapted. We evaluated the generality of the protocol by applying it to SOMns, a Newspeak implementation, which supports a diversity of concurrency models including communicating sequential processes, communicating event loops, threads and locks, fork/join parallelism, and software transactional memory. We implemented 21 breakpoints and 20 stepping operations for these concurrency models. For none of these, the debugger needed to be changed. Furthermore, we visualize all concurrent interactions independently of a specific concurrency model. To show that tooling for a specific concurrency model is possible, we visualize actor turns and message sends separately.
Deep Mutual Learning
Venue: arXiv cs.CV 2017
Year: 2017

Model distillation is an effective and widely used technique to transfer knowledge from a teacher to a student network. The typical application is to transfer from a powerful large network or ensemble to a small network, that is better suited to low-memory or fast execution requirements. In this paper, we present a deep mutual learning (DML) strategy where, rather than one way transfer between a static pre-defined teacher and a student, an ensemble of students learn collaboratively and teach each other throughout the training process. Our experiments show that a variety of network architectures benefit from mutual learning and achieve compelling results on CIFAR-100 recognition and Market-1501 person re-identification benchmarks. Surprisingly, it is revealed that no prior powerful teacher network is necessary -- mutual learning of a collection of simple student networks works, and moreover outperforms distillation from a more powerful yet static teacher.
Localizacao 3D em sistemas RFID com leitor movel
Venue: arXiv cs.NI 2017
Year: 2017

RFID has been widely used in applications for indoor objects location. This article proposes a 3D location algorithm based on a system that uses a mobile reader and a reference matrix of real (passive) and virtual tags. The proposed algorithm compares the RSSI of the target tag with the RSSI of the tags of the reference matrix and defines the estimated position. Preliminary results show that the location scheme may be promising for indoor location of tags.
F-index of graphs based on four operations related to the lexicographic product
Venue: arXiv cs.DM 2017
Year: 2017

The forgotten topological index or F-index of a graph is defined as the sum of cubes of the degree of all the vertices of the graph. In this paper we study the F-index of four operations related to the lexicographic product on graphs which were introduced by Sarala et al. [D. Sarala, H. Deng, S.K. Ayyaswamya and S. Balachandrana, The Zagreb indices of graphs based on four new operations related to the lexicographic product, \textit{Applied Mathematics and Computation}, 309 (2017) 156--169.].